Improvements to osc (especially with regards to the Git workflow) by mcepl

Description

There is plenty of hacking on osc, where we could spent some fun time. I would like to see a solution for https://github.com/openSUSE/osc/issues/2006 (which is sufficiently non-serious, that it could be part of HackWeek project).

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Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil

Join the Gitter channel! https://gitter.im/uyuni-project/hackweek

Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!

Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.

For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.

No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)

The idea is testing Salt and Salt-ssh clients, but NOT traditional clients, which are deprecated.

To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):

1. Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
2. Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
3. Package management (install, remove, update...)
4. Patching
5. Applying any basic salt state (including a formula)
6. Salt remote commands
7. Bonus point: Java part for product identification, and monitoring enablement
8. Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
9. Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
10. Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)

• If you don't have knowledge about some of the steps: ask the team
• If you still don't know what to do: switch to another distribution and keep testing.

This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)

Pending

Debian 13

The new version of the beloved Debian GNU/Linux OS

• [ ] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
• W] Onboarding (salt minion from UI, salt minion from bootstrap script, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
• [ ] Package management (install, remove, update...)
• [ ] Patching (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already). Probably not for Debian as IIRC we don't support patches yet.
• [ ] Applying any basic salt state (including a formula)
• [ ] Salt remote commands
• [ ] Bonus point: Java part for product identification, and monitoring enablement
• [ ] Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
• [ ] Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
• [ ] Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

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Create a page with all devel:languages:perl packages and their versions by tinita

Description

Perl projects now live in git: https://src.opensuse.org/perl

It would be useful to have an easy way to check which version of which perl module is in devel:languages:perl. Also we have meta overrides and patches for various modules, and it would be good to have them at a central place, so it is easier to lookup, and we can share with other vendors.

I did some initial data dump here a while ago: https://github.com/perlpunk/cpan-meta

But I never had the time to automate this.

I can also use the data to check if there are necessary updates (currently it uses data from download.opensuse.org, so there is some delay and it depends on building).

Goals

• Have a script that updates a central repository (e.g. https://src.opensuse.org/perl/_metadata) with metadata by looking at https://src.opensuse.org/perl/_ObsPrj (check if there are any changes from the last run)
• Create a HTML page with the list of packages (use Javascript and some table library to make it easily searchable)

Resources

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Switch software-o-o to parse repomd data by hennevogel

Run non-stop in escape road, avoid dangerous traps and test your speed to win spectacularly.

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Bugzilla goes AI - Phase 1 by nwalter

Description

This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.

Goals

To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.

Project Charter

https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description

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The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio

Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it.

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The Plan

Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!

Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:

❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.

❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.

❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.

If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.

Why?

We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

The Outputs

❥ A "State of the Agentic Union" for SUSE engineers, detailing what works, what explodes, and how much coffee we can drink while the robots do the rebasing.

❥ Honest, Daily Updates With All the Gory Details

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"what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai

Description

Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

Goals

• The user can run a command from the console, to check on a file or directory
• The filemanager contains the "analyze" feature within the context menu
• The local LLM could serve for other use cases where privacy matters

TBD

• Find or write capable one-shot and interactive MCP client
• Find or write simple+secure file access MCP server
• Create local LLM service with appropriate footprint, containerized
• Shell command with options
• KDE integration (Dolphin)
• Package
• Document

Resources

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AI-Powered Unit Test Automation for Agama by joseivanlopez

The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:

• Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
• TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
• Ruby: Integrates existing, robust YaST libraries (e.g., yast-storage-ng) to reuse established functionality.

The Problem: Testing Overhead

Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.

The Solution: AI-Driven Automation

This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:

1. Automatically generate new unit tests as code is developed.
2. Intelligently correct and update existing unit tests when the application code changes.

By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.

Goals

• Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g., gemini-cli) to automatically generate unit tests.
• Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
• Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.

Contribution & Resources

We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.

If you want to dive deep into AI for software quality, please reach out and join the effort!

• Authorized AI Tools: Tools supported by SUSE (e.g., gemini-cli)
• Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.

Interesting Links

• goose

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Gemini-Powered Socratic Bug Evaluation and Management Assistant by rtsvetkov

Description

To build a tool or system that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

Goals

Set up a Python environment

Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

Build the Dialogue Loop

1. Write a basic Python script using the Gemini API.
2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

Socratic Strategy Implementation

1. Refine the logic to ensure the questions follow a Socratic path (e.g., from symptom-> context -> assumptions -> root cause).
2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."

Resources

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Gemini-Powered Socratic Bug Evaluation and Management Assistant by rtsvetkov

Description

To build a tool or system that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

Goals

Set up a Python environment

Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

Build the Dialogue Loop

1. Write a basic Python script using the Gemini API.
2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

Socratic Strategy Implementation

1. Refine the logic to ensure the questions follow a Socratic path (e.g., from symptom-> context -> assumptions -> root cause).
2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."

Resources

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Minimal neovim LSP setup, without any external plugin. by wqu_suse

Description

Neovim is getting more and more built-in features, from LSP client, snippet to auto-completion.

Now it's possible to built a neovim IDE environment, with built-in lsp, snippet and auto-completion, without any external plugin.

Goals

Use a minimal init.lua only, without any nvim package manager nor external plugin, to build an IDE environment, which can:

• Use LSP to do context aware context lookup
• Auto-complete function name, parameter list etc
• Support multiple LSP servers for different languages

Linux kernel and btrfs-progs will be used as the example projects.

Resources

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